{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": "## 折线图的绘制"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "          DATE  VALUE\n0   1948-01-01    3.4\n1   1948-02-01    3.8\n2   1948-03-01    4.0\n3   1948-04-01    3.9\n4   1948-05-01    3.5\n5   1948-06-01    3.6\n6   1948-07-01    3.6\n7   1948-08-01    3.9\n8   1948-09-01    3.8\n9   1948-10-01    3.7\n10  1948-11-01    3.8\n11  1948-12-01    4.0\n         DATE  VALUE\n0  1948-01-01    3.4\n1  1948-02-01    3.8\n2  1948-03-01    4.0\n3  1948-04-01    3.9\n4  1948-05-01    3.5\n5  1948-06-01    3.6\n6  1948-07-01    3.6\n7  1948-08-01    3.9\n8  1948-09-01    3.8\n9  1948-10-01    3.7\n10 1948-11-01    3.8\n11 1948-12-01    4.0\n"
          ],
          "output_type": "stream"
        }
      ],
      "source": "# max.to_datetime 将时间格式化\nimport pandas as pd\nimport matplotlib.pyplot as plt\nunrate \u003d pd.read_csv(\u0027unrate.csv\u0027)  # 月度失业率\nprint(unrate.head(12))\nunrate[\u0027DATE\u0027] \u003d pd.to_datetime(unrate[\u0027DATE\u0027])\nprint(unrate.head(12))\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
            "image/png": "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\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "# 画图\nimport pandas as pd\nfirs_twelev \u003d unrate[0:12]\nimport matplotlib.pyplot as plt\nplt.plot(firs_twelev[\u0027DATE\u0027], firs_twelev[\u0027VALUE\u0027]) # plot的参数, 前为x轴数据,后为y轴数据\nplt.xticks(rotation\u003d45) # 将x轴刻度倾斜标准\nplt.xlabel(\"month\")\nplt.ylabel(\"unrate\")\nplt.title(\"the unrate of per month\")\nplt.show()",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% \n",
          "is_executing": false
        }
      }
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}